Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity. Author Summary We use microstructural MRI and resting-state MEG data to investigate the relationship between the brain’s structure and function. We construct functional brain networks by calculating correlations between the Hilbert envelope of the beamformer time series in different brain areas. We also construct structural brain networks using tractography, for five different edge weightings (number of streamlines, fractional anisotropy, myelination, radial diffusivity, and a binary weighting). Those structural networks are then used in function-predicting algorithms, and the predicted functional networks are compared to the measured ones. We observe that the shortest-path-length algorithm is better at predicting the observed patterns of functional connectivity, and that the number of streamlines and myelination are the edge weightings that lead to the highest correlations between the predicted and the observed functional connectivity.

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices. Author Summary We proposed an energy landscape method to quantify the occurrence probability of network states in magnetoencephalograph (MEG) oscillatory power during rest, which was derived from a pairwise maximum entropy model (pMEM). We compared the energy landscapes measures of three resting-state networks between patients with juvenile myoclonic epilepsy (JME) and healthy controls. The pMEM provided a good fit to the binarized MEG oscillatory power in both patients and controls. Patients with JME exhibited fewer local minima of the energy and elevated energy values than controls, predominately in the fronto-parietal network across multiple frequency bands. Furthermore, multivariate features constructed from energy landscapes allowed significant single-patient classification. Our results further highlighted the pMEM as a descriptive, generative, and predictive model for characterizing atypical functional network properties in brain disorders.